Hello everyone! In this article, I will show you how you can use tidyr for data manipulation. tidyr is a package by Hadley Wickham that makes it easy to tidy your data. It is often used in conjunction with dplyr. Data is said to be tidy when each column represents a variable, and each row represents an observation.
Bayes’s theorem, touted as a powerful method for generating knowledge, can also be used to promote superstition and pseudoscience
There are numerous strategies for dealing with non-proportional hazards in cox regression analysis. You can stratify your data based on a problematic variable. You can chuck the cox model and create ‘pseudo-observations’ to analyze the gains (or losses) in lifetime within a certain period associated with changes in a variable. If age is a problem (unlikely for customer churn, but it happens a lot in medical contexts), you can use age rather than time in the cohort as your time scale. The list goes on. But this is statistics! We’re supposed to be modeling things!
There are many ways to compute the best solution to a problem, but not all of them can be put into production. The Portable Format for Analytics (PFA) provides a way of formalizing and moving models.